11 citations · 28 across the 7 of their papers we have counts for
7 papers
Model reduction for a power grid model
Jing Li, Panos Stinis
We apply model reduction techniques to the DeMarco power grid model. The DeMarco model, when augmented by an appropriate line failure mechanism, can be used to study cascade failur…
Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise
Panos Stinis, Huan Lei, Jing Li +1
Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properti…
A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness
Huan Lei, Jing Li, Peiyuan Gao +2
The challenge of quantifying uncertainty propagation in real-world systems is rooted in the high-dimensionality of the stochastic input and the frequent lack of explicit knowledge…
Mori-Zwanzig reduced models for uncertainty quantification
Jing Li, Panos Stinis
In many time-dependent problems of practical interest the parameters and/or initial conditions entering the equations describing the evolution of the various quantities exhibit unc…
Efficient failure probability calculation through mesh refinement
Jing Li, Panos Stinis
We present a novel way of accelerating hybrid surrogate methods for the calculation of failure probabilities. The main idea is to use mesh refinement in order to obtain improved lo…
A unified framework for mesh refinement in random and physical space
Jing Li, Panos Stinis
In recent work we have shown how an accurate reduced model can be utilized to perform mesh refinement in random space. That work relied on the explicit knowledge of an accurate red…
Mesh refinement for uncertainty quantification through model reduction
Jing Li, Panagiotis Stinis
We present a novel way of deciding when and where to refine a mesh in probability space in order to facilitate the uncertainty quantification in the presence of discontinuities in…